retry_pipeline_execution ¶

def retry_pipeline_execution(
    self,
    *,
    config_overrides: Optional[SageMakerClientConfig] = None,
    pipeline_execution_arn: Optional[
        "capo_sagemaker.types.pipeline_execution_arn.PipelineExecutionArn"
    ] = None,
    client_request_token: Optional[
        "capo_sagemaker.types.idempotency_token.IdempotencyToken"
    ] = None,
    parallelism_configuration: Optional[
        "capo_sagemaker.types.parallelism_configuration.ParallelismConfiguration"
    ] = None,
) -> "capo_sagemaker.types.retry_pipeline_execution_response.RetryPipelineExecutionResponse":
    """<p>Retry the execution of the pipeline.</p>

    Args:
        pipeline_execution_arn: <p>The Amazon Resource Name (ARN) of the pipeline execution.</p>
        client_request_token: <p>A unique, case-sensitive identifier that you provide to ensure the idempotency of the operation. An idempotent operation completes no more than once.</p>
        parallelism_configuration: <p>This configuration, if specified, overrides the parallelism configuration of the parent pipeline.</p>

    Raises:
        capo_sagemaker.errors.conflict_exception.ConflictException: <p>There was a conflict when you attempted to modify a SageMaker entity such as an <code>Experiment</code> or <code>Artifact</code>.</p>
        capo_sagemaker.errors.resource_limit_exceeded.ResourceLimitExceeded: <p> You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created. </p>
        capo_sagemaker.errors.resource_not_found.ResourceNotFound: <p>Resource being access is not found.</p>
        capo_sagemaker.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    def _handler(
        req: "OperationRequest[capo_sagemaker.types.retry_pipeline_execution_request.RetryPipelineExecutionRequest]",
    ) -> OperationResponse[
        "capo_sagemaker.types.retry_pipeline_execution_response.RetryPipelineExecutionResponse"
    ]:
        import capo_sagemaker._operations.sage_maker.retry_pipeline_execution

        output, http_response = (
            capo_sagemaker._operations.sage_maker.retry_pipeline_execution.retry_pipeline_execution(
                req.options, req.input
            )
        )
        return OperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("RetryPipelineExecution")
    )
    input_: capo_sagemaker.types.retry_pipeline_execution_request.RetryPipelineExecutionRequest = {}
    if pipeline_execution_arn is not None:
        input_["pipeline_execution_arn"] = pipeline_execution_arn
    if client_request_token is None:
        client_request_token = str(uuid.uuid4())
    input_["client_request_token"] = client_request_token
    if parallelism_configuration is not None:
        input_["parallelism_configuration"] = parallelism_configuration

    response = execute_pipeline(
        OperationRequest(input=input_, options=options_),
        handler=_handler,
        interceptors=list(interceptors_),
    )
    response.response.close()
    return response.output